A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information

Surface ozone (O3) is considered an hazard to human health, affecting vegetation crops and ecosystems. Accurate time and location O3 forecasting can help to protect citizens to unhealthy exposures when high levels are expected. Usually, forecasting models use numerous O3 precursors as predictors, limiting the reproducibility of these models to the availability of such information from data providers. This study introduces a 24 h-ahead hourly O3 concentrations forecasting methodology based on bagging and ensemble learning, using just two predictors with lagged O3 concentrations. This methodology was applied on ten-year time series (2006–2015) from three major urban areas of Andalusia (Spain). Its forecasting performance was contrasted with an algorithm especially designed to forecast time series exhibiting temporal patterns. The proposed methodology outperforms the contrast algorithm and yields comparable results to others existing in literature. Its use is encouraged due to its forecasting performance and wide applicability, but also as benchmark methodology

Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula

This work explores the use of different seismicity indicators as inputs for artificial neural networks. The combination of multiple indicators that have already been successfully used in different seismic zones by the application of feature selection techniques is proposed. These techniques evaluate every input and propose the best combination of them in terms of information gain. Once these sets have been obtained, artificial neural networks are applied to four Chilean zones (the most seismic country in the world) and to two zones of the Iberian Peninsula (a moderate seismicity area). To make the comparison to other models possible, the prediction problem has been turned into one of classification, thus allowing the application of other machine learning classifiers. Comparisons with original sets of inputs and different classifiers are reported to support the degree of success achieved. Statistical tests have also been applied to confirm that the results are significantly different than those of other classifiers. The main novelty of this work stems from the use of feature selection techniques for improving earthquake prediction methods. So, the infor-mation gain of different seismic indicators has been determined. Low ranked or null contribution seismic indicators have been removed, optimizing the method. The optimized prediction method proposed has a high performance. Finally, four Chilean zones and two zones of the Iberian Peninsula have been charac-terized by means of an information gain analysis obtained from different seismic indicators. The results confirm the methodology proposed as the best features in terms of information gain are the same for both regions.Ministerio de Ciencia y Tecnología BIA2004-01302Ministerio de Ciencia y Tecnología TIN2011-28956-C02-01Junta de Andalucía P11-TIC-752

Effect of nest composition, experience and nest quality on nest-building behaviour in the Bonelli's Eagle.

In bi-parentally built nests, there is evidence to suggest that nests are extended phenotypic signals that accurately indicate the quality of the building parent/s. Raptors often use a variety of materials to build their nests (natural, such as branches, but also non-natural objects), presumably due to their insulating properties, their suitability to advertise occupancy of the nest, and to decrease pathogen and parasite loads. However, in raptors where both sexes collaborate in nest construction, it is unclear whether nest building (taking the amount of material carried to the nest as the potential predictor) is an indicator of parental quality, and whether the effort expended by both sexes could constitute an honest signal of parental quality to their partners. Between 2011 and 2016, we monitored 16 nests of Bonelli's Eagles (Aquila fasciata), and we examined data on sex, type of material brought to the nest, breeding experience, nest quality, timing, and nest-building investment prior to egg-laying from 32 identifiable Bonelli's Eagles during the pre-laying period to investigate the relative contribution of the sexes to the amount of nest material gathered. Our results indicate that sex is not a determining factor in nest-building effort, and that females did not increase their parental effort in response to the male's contribution, and supply of materials did not increase during the pre-laying period. In contrast, our models showed that: (1) the type of material supplied to the nest by both sexes varied significantly throughout the pre-laying period and (2) nest-building effort was determined by individual experience and nest quality. Therefore, our study suggests that male nest-building behaviour and investment by Bonelli's Eagles cannot be considered as an extended phenotypic signal. The differential use of hard and green material by both sexes in the early and late stages of nest-building period, and the fact that the more experienced individuals contributed a larger amount of material on low quality nests, are discussed in the contexts of signaling nest occupancy to conspecifics and competitors and the decrease of ectoparasite loads during the pre-laying period

A novel tree-based algorithm to discover seismic patterns in earthquake catalogs

A novel methodology is introduced in this research study to detect seismic precursors. Based on an existing approach, the new methodology searches for patterns in the historical data. Such patterns may contain statistical or soil dynamics information. It improves the original version in several aspects. First, new seismicity indicators have been used to characterize earthquakes. Second, a machine learning clustering algorithm has been applied in a very flexible way, thus allowing the discovery of new data groupings. Third, a novel search strategy is proposed in order to obtain non-overlapped patterns. And, fourth, arbitrary lengths of patterns are searched for, thus discovering long and short-term behaviors that may influence in the occurrence of medium-large earthquakes. The methodology has been applied to seven different datasets, from three different regions, namely the Iberian Peninsula, Chile and Japan. Reported results show a remarkable improvement with respect to the former version, in terms of all evaluated quality measures. In particular, the number of false positives has decreased and the positive predictive values increased, both of them in a very remarkable manner.Ministerio de Ciencia y Tecnología TIN2011-28956-C00Junta de Andalucía P12-TIC-1728Instituto Ramón y Cajal (RYC) RYC-2012-1198

Physical rehabilitation in football by mechanical vibration and hypoxia

Las acciones explosivas en el fútbol se triplican respecto a los años 60, llegando a las 200-215 acciones explosivas/partido. Esto supone que la potencia muscular y la capacidad de recuperación sean factores limitantes, pudiendo ser frecuentes las lesiones musculares. Durante la lesión se pierden las cualidades condicionales, menos cuanto más corto sea este periodo. Existen diversos métodos para la mejora de la fuerza y capacidad de recuperación mediante las plataformas vibratorias y la hipoxia intermitente (HI). Mostramos resultados de una intervención con plataforma vibratoria y HI en futbolistas convalescientes de una rotura fibrilar. Este nuevo modelo de entrenamiento puede permitir mejoras la fuerza máxima (p<0,05) y capacidadde recuperación (p<0,05) ayudando en gran medida a no perder las cualidades condicionales.Explosive actions in football are three times over 60 years, reaching 200-215 explosive actions /match. This means that for an elite player, muscular power and resilience are performance limiting factors, which may be frequent muscular injuries. During the injury, conditional qualities are lost, the less the shorter the period. There are several methods for improving the strength and resilience, emphasizing the body vibration training and intermittent hypoxia (IH). In this study, results of an intervention HI vibrating platform and players who have been convalescing from a hamstring injury are shown. The results obtained suggest that this new training model allows for improvements in the levels of maximum force (p <0.05) and resilience (p <0.05). This helps keep the conditional qualities greatly

Remote mining: from clustering to DTM

LIDAR data acquisition is becoming an indispensable task for terrain characterization in large surfaces. In Mediterranean woods this job results hard due to the great variety of heights and forms, as well as sparse vegetation that they present. A new data mining-based approach is proposed with the aim of classifying LIDAR data clouds as a first step in DTM generation. The developed methodology consists in a multi-step iterative process that splits the data into different classes (ground and low/med/high vegetation) by means of a clustering algorithm. This method has been tested on three different areas of the southern Spain with successful results, verging on 80% hitsMinisterio de Ciencia y Tecnología TIN2007-6808

A deep LSTM network for the Spanish electricity consumption forecasting

Nowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to the growth in electricity consumption in recent years, mainly in large cities, electricity forecasting is key to the management of an efficient, sustainable and safe smart grid for the consumer. In this work, a deep neural network is proposed to address the electricity consumption forecasting in the short-term, namely, a long short-term memory (LSTM) network due to its ability to deal with sequential data such as time-series data. First, the optimal values for certain hyper-parameters have been obtained by a random search and a metaheuristic, called coronavirus optimization algorithm (CVOA), based on the propagation of the SARS-Cov-2 virus. Then, the optimal LSTM has been applied to predict the electricity demand with 4-h forecast horizon. Results using Spanish electricity data during nine years and half measured with 10-min frequency are presented and discussed. Finally, the performance of the proposed LSTM using random search and the LSTM using CVOA is compared, on the one hand, with that of recently published deep neural networks (such as a deep feed-forward neural network optimized with a grid search) and temporal fusion transformers optimized with a sampling algorithm, and, on the other hand, with traditional machine learning techniques, such as a linear regression, decision trees and tree-based ensemble techniques (gradient-boosted trees and random forest), achieving the smallest prediction error below 1.5%.Data Science and Big Data La

Removal of a mixture of oxygenated VOCs in a biotrickling filter

[Abstract] Laboratory scale-studies on the biodegradation of a 1:1:1 wt mixture of three oxygenated volatile organic compounds (VOCs), ethanol, ethyl acetate and methyl-ethyl ketone (MEK) in a biotrickling filter were carried out using two identically sized columns, filled with different polypropylene rings. The reactors were seeded with a two-month preconditioned culture from activated sludge. The performance of the biotrickling filters was examined for a continuous period of 4 months at VOC concentration from 125 mg-C/m3 to 550 mg-C/m3 and at gas flow rates of around 1.0 m3/h, 2.0 m3/h and 4.6 m3/h, which correspond to gas empty bed residence times (EBRT) of 68 s, 33 s and 16 s, respectively. Similar performance was obtained for both supports. Intermittent flow rate of trickling liquid was shown as beneficial to improve the removal efficiency of the system. A stratification in the substrate consumption was observed from gas composition profiles, with MEK % in the emission greater than 78%. Continuous VOC feeding resulted in an excessive accumulation of biomass and high pressure drop was developed in less than 20-30 days of operation. Intermittent VOC loading with night and weekend feed cut-off periods passing dried air, but without water addition, was shown as a successful operational mode to control the biofilm thickness. In this case, operation at high inlet loads was extended for more than 50 days maintaining high removal efficiencies and low pressure drops